Dataset Information

Impact of preoperative serum albumin on 30-day mortality following surgery for colorectal cancer: a population-based cohort study.

ABSTRACT:

Objective

Surgery is the only potentially curable treatment for colorectal cancer (CRC), but it is hampered by high mortality. Human serum albumin (HSA) below 35?g/L is associated with poor overall prognosis in patients with CRC, but evidence regarding the impact on postoperative mortality is sparse.

Methods

We performed a population-based cohort study including patients undergoing CRC surgery in North and Central Denmark (1997-2011). We categorised patients according to HSA concentration measured 1-30?days prior to surgery date. We used the Kaplan-Meier method to compute 30-day mortality and Cox regression model to compute HRs as measures of the relative risk of death, controlling for potential confounders. We further stratified patients by preoperative conditions, including cancer stage, comorbidity level, and C reactive protein concentration.

Results

Of the 9339 patients undergoing first-time CRC surgery with preoperative HSA measurement, 26.4% (n=2464) had HSA below 35?g/L. 30-day mortality increased from 4.9% among patients with HSA 36-40?g/L to 26.9% among patients with HSA equal to or below 25?g/L, compared with 2.0% among patients with HSA above 40?g/L. The corresponding adjusted HRs increased from 1.75 (95% CI 1.25 to 2.45) among patients with HSA 36-40?g/L to 7.59 (95% CI 4.95 to 11.64) among patients with HSA equal to or below 25?g/L, compared with patients with HSA above 40?g/L. The negative impact associated with a decrement of HSA was found in all subgroups.

Conclusions

A decrement in preoperative HSA concentration was associated with substantial concentration-dependent increased 30-day mortality following CRC surgery.

SUBMITTER: Montomoli J

PROVIDER: S-EPMC4599163 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Impact of preoperative serum albumin on 30-day mortality following surgery for colorectal cancer: a population-based cohort study.

Montomoli Jonathan J Erichsen Rune R Antonsen Sussie S Nilsson Tove T Sørensen Henrik Toft HT

BMJ open gastroenterology 20150804 1

<h4>Objective</h4>Surgery is the only potentially curable treatment for colorectal cancer (CRC), but it is hampered by high mortality. Human serum albumin (HSA) below 35 g/L is associated with poor overall prognosis in patients with CRC, but evidence regarding the impact on postoperative mortality is sparse.<h4>Methods</h4>We performed a population-based cohort study including patients undergoing CRC surgery in North and Central Denmark (1997-2011). We categorised patients according to HSA conce ...[more]

PMID: 26462287

Similar Datasets

Project description:BackgroundPostoperative mortality after colorectal cancer surgery varies across hospitals and countries. The aim of this study was to test the Association of Coloproctologists of Great Britain and Ireland (ACPGBI) models as predictors of 30-day mortality in an Australian cohort.MethodsData from patients who underwent surgery in six hospitals between 1996 and 2015 (CRC data set) were reviewed to test ACPGBI models, and patients from 79 hospitals in the Bi-National Colorectal Cancer Audit between 2007 and 2016 (BCCA data set) were analysed to validate model performance. Recalibrated models based on ACPGBI risk models were developed, tested and validated on a data set of Australasian patients.ResultsOf 18 752 patients observed during the study, 6727 (CRC data set) and 3814 (BCCA data set) were analysed. The 30-day mortality rate was 1·1 and 3·5 per cent in the CRC and BCCA data sets respectively. Both the original and revised ACPGBI models overestimated 30-day mortality for the CRC data set (observed to expected (O/E) ratio 0·17 and 0·21 respectively). Their ability to correctly predict mortality risk was poor (P < 0·001, Hosmer-Lemeshow test); however, the area under the curve for both models was 0·88 (95 per cent c.i. 0·85 to 0·92) showing good discriminatory power to classify 30-day mortality. The recalibrated original model performed well for calibration and discrimination, whereas the recalibrated revised model performed well for discrimination but not for calibration. Risk prediction was good for both recalibrated models. On external validation using the BCCA data set, the recalibrated models underestimated mortality risk (O/E ratio 3·06 and 2·98 respectively), whereas both original and revised ACPGBI models overestimated the risk (O/E ratio 0·48 and 0·69). All models showed similar good discrimination.ConclusionThe original and revised ACPGBI models overpredicted risk of 30-day mortality. The new Australasian calibrated ACPGBI model needs to be tested further in clinical practice.

Project description:BackgroundDespite novel improvements in the diagnosis and treatment of infective endocarditis (IE), there has been no significant improvement in the survival rate of IE, which indicates that many details still need to be optimized in the preoperative assessment. We sought to evaluate preoperative serum albumin as a biomarker for predicting early mortality after IE surgery.MethodsBetween October 2013 and June 2019, patients with a definite diagnosis of IE were enrolled in this study. Patients' albumin levels at admission were used as the preoperative albumin levels. Restricted cubic spline and multivariate logistic regression analyses were performed to evaluate the relationship between albumin and early mortality. Receiver operating characteristic curve analyses were performed to assess the role of albumin in predicting early mortality and compare the predictive capacity of traditional models with models that included albumin.ResultsOf the 276 IE patients, 20 (7.2%) died in hospital or within 30 days of surgery. Hypoalbuminemia (an albumin level <3.5 g/dL) was present in 109 (39.5%) patients. The multivariate logistic regression analysis showed that preoperative albumin was inversely associated with early mortality [adjusted odds ratio (OR) =0.22 per 1 g/dL, 95% confidence interval (CI): 0.07-0.65, P=0.006] after full adjustment. Preoperative albumin had value in predicting early mortality [area under the curve (AUC) =0.72, 95% CI: 0.61-0.84; P<0.01]. After adding albumin to the European System for Cardiac Operative Risk Evaluation (EuroSCORE) and Charlson score, the predictive ability of the model was further improved (EuroSCORE II: AUC =0.55; 95% CI: 0.42-0.67 to AUC =0.72; 95% CI: 0.61-0.84; Charlson score: AUC =0.73; 95% CI: 0.64-0.83 to AUC =0.78; 95% CI: 0.68-0.88).ConclusionsPreoperative serum albumin is inversely associated with early mortality after IE surgery, and is a promising prognostic indicator in preoperative risk stratification assessments of IE patients.

Project description:ImportanceQuality improvement programs for colorectal cancer surgery have been introduced with benchmarking based on quality indicators, such as mortality. Detailed (pre)operative characteristics may offer relevant information for proper case-mix correction.ObjectiveTo investigate the added value of machine learning to predict quality indicators for colorectal cancer surgery and identify previously unrecognized predictors of 30-day mortality based on a large, nationwide colorectal cancer registry that collected extensive data on comorbidities.Design, setting, and participantsAll patients who underwent resection for primary colorectal cancer registered in the Dutch ColoRectal Audit between January 1, 2011, and December 31, 2016, were included. Multiple machine learning models (multivariable logistic regression, elastic net regression, support vector machine, random forest, and gradient boosting) were made to predict quality indicators. Model performance was compared with conventionally used scores. Risk factors were identified by logistic regression analyses and Shapley additive explanations (ie, SHAP values). Statistical analysis was performed between March 1 and September 30, 2020.Main outcomes and measuresThe primary outcome of this cohort study was 30-day mortality. Prediction models were trained on a training set by performing 5-fold cross-validation, and outcomes were measured by the area under the receiver operating characteristic curve on the test set. Machine learning was further used to identify risk factors, measured by odds ratios and SHAP values.ResultsThis cohort study included 62 501 records, most patients were male (35 116 [56.2%]), were aged 61 to 80 years (41 560 [66.5%]), and had an American Society of Anesthesiology score of II (35 679 [57.1%]). A 30-day mortality rate of 2.7% (n = 1693) was found. The area under the curve of the best machine learning model for 30-day mortality (0.82; 95% CI, 0.79-0.85) was significantly higher than the American Society of Anesthesiology score (0.74; 95% CI, 0.71-0.77; P < .001), Charlson Comorbidity Index (0.66; 95% CI, 0.63-0.70; P < .001), and preoperative score to predict postoperative mortality (0.73; 95% CI, 0.70-0.77; P < .001). Hypertension, myocardial infarction, chronic obstructive pulmonary disease, and asthma were comorbidities with a high risk for increased mortality. Machine learning identified specific risk factors for a complicated course, intensive care unit admission, prolonged hospital stay, and readmission. Laparoscopic surgery was associated with a decreased risk for all adverse outcomes.Conclusions and relevanceThis study found that machine learning methods outperformed conventional scores to predict 30-day mortality after colorectal cancer surgery, identified specific patient groups at risk for adverse outcomes, and provided directions to optimize benchmarking in clinical audits.

Dataset Information

Impact of preoperative serum albumin on 30-day mortality following surgery for colorectal cancer: a population-based cohort study.

Objective

Methods

Results

Conclusions

Publications

Impact of preoperative serum albumin on 30-day mortality following surgery for colorectal cancer: a population-based cohort study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets